Adaptive information processing systems, methods, and media for updating product documentation and knowledge base

ABSTRACT

An adaptive information processing system for updating product documentation and associated knowledge base is disclosed, the system including at least one subsystem for receiving original data from a data source, and a central dynamic data system to integrate the original data from the at least one subsystem. The central dynamic data system is configured to integrate system knowledge with the original data to form integrated data, wherein the central dynamic data system is configured to dynamically update the product documentation and the knowledge base based on the integrated data. A computer implemented method for dynamically updating product documentation and knowledge base is further disclosed, the method includes receiving original data from a data source, and integrating the knowledge base with the original data from the data source to form integrated data. The method further includes mapping the integrated data with at least one application and dynamically updating product documentation and the knowledge base based on the integrated data.

TECHNICAL FIELD

The present disclosure relates generally to the field of informationtechnology, and more specifically, to methods, systems, and media forintegrating information related to automotive parts and services.

BACKGROUND

As the value and use of information continues to increase, individualsand businesses seek additional ways to process and manage information.Particularly, with tremendous growth in business information, in themagnitude of terabytes, for example, businesses seek adaptive andevent-driven information technology to process (e.g., integrate, manage,analyze) the widely distributed data sources for domain dependentapplications.

With current information technology, product documentation or datarelated to goods and services, such as repair documentation or technicaldocumentation, may be static and/or domain dependent with longprocedures and transcription errors. Oftentimes, current productdocumentation may not get updated until the next version ofdocumentation is released. Also, current information technology may notpresent the most proper or efficient connection among subsystems orprocesses which derive or process the data to become incorporated intoproduct documentation. In an industry such as automotive services, forexample, subsystems or processes such as warranty, logistics, fieldservice, and technical documentation, may not be properly connected toknowledge/data sources such as domain semantics, technical knowledge,natural text verbatim, web data, sensor data, and parts data.Alternatively, the aforementioned subsystems/processes may not beproperly connected to objectives such as quality improvement, costreduction, early issue identification, or the like.

Thus, a need exists for an improved knowledge-driven adaptive servicesystems, methods, and media to process data among domain dependentapplications to improve the methodology for the creation and/or updatingof product documentation and knowledge base activities. Such systems,methods, and media may efficiently provide information processing amongvarious segments of an automotive service chain such as productdocumentation, field service, warranty analysis, and logistics toproperly align them with their respective data resources and objectives.

SUMMARY

The following presents a general summary of several aspects of thedisclosure in order to provide a basic understanding of at least someaspects of the disclosure. This summary is not an extensive overview ofthe disclosure nor is it intended to identify key elements of thedisclosure or to delineate the scope of the claims. The followingsummary merely presents some concepts of the disclosure in a generalform as a prelude to the more detailed description that follows.

One aspect of the present disclosure provides an adaptive informationprocessing system for updating product documentation and associatedknowledge base, the system including at least one subsystem forreceiving original data from a data source, and a central dynamic datasystem to integrate data from the data source. The central dynamic datasystem is configured to integrate the knowledge base with the originaldata to form integrated data, and wherein the central dynamic datasystem is further configured to dynamically update the productdocumentation and the associated knowledge base based on the integrateddata.

Another aspect of the present disclosure provides a computer implementedmethod for dynamically updating product documentation and knowledgebase, the method including receiving original data from a data source,and integrating the knowledge base with the original data from the datasource to form integrated data. The method further includes mapping theintegrated data with at least one application and dynamically updatingproduct documentation and the knowledge base based on the integrateddata.

Another aspect of the present disclosure provides a computer implementedmethod for dynamically updating automotive repair documentation, themethod including receiving original data from a data source, andintegrating the original data with the knowledge base to dynamicallyupdate the automotive repair documentation.

Another aspect of the present disclosure provides for acomputer-readable medium having instructions stored thereon that whenexecuted causes a computer to execute a method for dynamically updatingproduct documentation and knowledge base. The method includes receivingoriginal data from a data source and integrating the knowledge base withthe original data from the data source to form integrated data. Themethod further includes mapping the integrated data with at least oneapplication and dynamically updating the product documentation and theknowledge base based on the integrated data.

Yet another aspect of the present disclosure provides for acomputer-readable medium having instructions stored thereon that whenexecuted causes a computer to execute a method for dynamically updatingautomotive repair documentation and knowledge base, the method includingreceiving original data from a data source, and integrating the originaldata with the knowledge base to dynamically update the automotive repairdocumentation.

BRIEF DESCRIPTION OF THE DRAWINGS

For detailed understanding of the present disclosure, references shouldbe made to the following detailed description of the several aspects,taken in conjunction with the accompanying drawings, in which likeelements have been given like numerals and wherein:

FIG. 1 illustrates an adaptive service system in accordance with oneaspect of the present disclosure;

FIG. 2 illustrates a data warehouse for use with the adaptive servicesystem in FIG. 1;

FIG. 3A illustrates a domain taxonomic structure for use with theadaptive service system in FIG. 1;

FIG. 3B illustrates a domain taxonomic structure for use with theadaptive service system in FIG. 1;

FIG. 4 provides a schematic for data analysis to implement the adaptiveservice system in FIG. 1; and

FIG. 5 illustrates a portal for receiving data into the adaptive servicesystem in FIG. 1.

DETAILED DESCRIPTION

Before the present methods, systems, and computer-readable storage mediaare described, it is to be understood that this disclosure is notlimited to the particular methods, systems, and media described, as suchmay vary. Also, the present disclosure is not limited in its applicationto the details of construction, arrangement or order of componentsand/or steps set forth in the following description or illustrated inthe figures. Thus, the disclosure is capable of other aspects,embodiments or implementations or being carried out/practiced in variousother ways.

One of ordinary skill in the art should understand that the terminologyused herein is for the purpose of describing possible aspects,embodiments and/or implementations only, and is not intended to limitthe scope of the present disclosure which will be limited only by theappended claims. Further, use of terms such as “including”,“comprising”, “having”, “containing”, “involving”, “consisting”, andvariations thereof are meant to encompass the listed thereafter andequivalents thereof as well as additional items.

It must also be noted that as used herein and in the appended claims,the singular forms “a,” “and,” and “the” may include plural referentsunless the context clearly dictates otherwise. Thus, for example,reference to “a system” refers to one or several systems and referenceto “a method of integrating” includes reference to equivalent steps andmethods known to those skilled in the art, and so forth.

Turning now to FIG. 1, a schematic is provided of an adaptive servicesystem, indicated generally at 100, as one implementation of aninformation processing system. Although the present disclosure describesthe adaptive service system 100 in relation to automotive services(e.g., repair services), it should be understood that methods, systems,and media of the present disclosure may have applicability to anyadaptive service system which dynamically receives and processes datafrom multiple subsystems or data sources. Further, references madeherein to products or services may correlate specifically to vehicles orrepair services, respectively. The dynamic nature of the adaptiveservice system 100 may allow the system to adapt and integrate originaldata based on changes in incoming data over time. As shown, the adaptiveservice system 100 may comprise various building blocks of service, alsoreferred to herein as subsystems to receive original data from datasources, including product documentation 120, field data processing 130,warranty analysis 150, and logistics 160.

Product documentation 120 may include repair and/or product manuals, forexample. In the event when an automotive or vehicular product isdeveloped, even prior to sale to a customer, product or repair manualsmay be created. The manuals may be dynamically updated, through adaptivedocumentation 122 (i.e., updating of documentation), from original datareceived from an internal department (e.g., engineering, design,development, manufacturing, sales, service) within an automotive companyutilizing a meta data (e.g., an extensible markup language (XML))database to store and model a repair procedure. As used herein,“adaptive” may refer to any methodology not requiring human or manualinput/intervention but rather driven by the receipt of some type ofdata. Product documentation 120 may be created using semanticrelationships (discussed in detail below) whereby common concepts areshared by various types of documentation (e.g., repair manuals, usermanuals, product manuals, technician courses). Multi-media mining may bea technique employed to identify common and sharable concepts to createthe semantic relationships. Repair procedures, hierarchical domainstructures, and information reflecting domain terminology may bemaintained in the system knowledge (discussed below) of the adaptiveservice system 110. The product documentation is thus automatically(i.e., adaptively) compiled based on a given application and/or product(e.g., vehicle) using the system knowledge, thus reducing redundantdocumentation.

Product documentation 120 may be characterized by adaptive documentation122, via changes in vehicle design or through knowledge acquired by theadaptive service system 100. Automated reporting 124 may further begenerated reflecting the changes for providing feedback to design 110 orto the adaptive service system 100.

Another example of a data source may include field data 130, wherebyoriginal data in the form of field cases (e.g., repair cases) arereceived and analyzed using case based reasoning and/or learningalgorithms. Generally, field data processing 130 may utilize originaldata from various sources, such as an XML database or from a semanticnetwork (to be described below) or original data collected from theinternet, in the form of unstructured data, for example. As an example,an automated search 134, used to retrieve knowledge from documentationor previous repair cases, may help dealer technicians search forrelevant repair procedures and provide suggestions linking the knowledgeto a given repair procedure.

Field data processing 130 may further comprise dynamic guidance fortrouble-shooting 132 whereby questions are asked and dynamic guidance isprovided through trouble-shooting trees. The trouble-shooting trees areadapted to the skill level of technicians, based on his/her searchcriteria or answers to the questions asked, for example, and repairprocedures (e.g., steps, graphics) are provided accordingly. The repaircase base is dynamically updated and product documentation 120 may beupdated with the knowledge extracted, such as through the identificationof repair procedures by mining past repair cases or blogs.

Field cases utilized for field data processing 130 are tracked inreal-time to identify emerging or critical issues. Incoming repaircases, normally acquired through sources such as dealers,remote-services or the Internet, for example, may be processed foranalysis. During such processing step, natural language text processingtechniques may be used to standardize the natural language text receivedand extract concepts. Extracted concepts may include, but are notlimited to, named entities from condition, cause, remedy, repair textobserved symptom, part name, action taken by technician, or failuretype.

As an example of field data processing 130, a network of dealers maycompile information regarding vehicle models which experience brakefailure. The information compiled may comprise the method in which eachtechnician diagnoses the part failure, thus making up the knowledgebase, i.e., knowledge acquired by the adaptive service system 100, forfield data processing. Knowledge base may be used interchangeably withsystem knowledge in the present disclosure to refer generally toknowledge acquired and integrated by the adaptive service system 100. Inthe present example, after accumulating information regarding a singleor multiple instances of brake failure, should a future brake failureoccur, a technician may refer to a past case to determine the method(s)for repairing brakes. Thus, repair documentation and knowledge baserelated to brakes may be adaptively updated following the accumulationof field data pertaining to brake failure.

As part of the adaptive service system 100, a central dynamic datasystem (not shown), including a processor or the like, may integrateoriginal data with the knowledge base, thus forming integrated data.Product documentation 120 and knowledge base, may in turn, may bedynamically updated by the central dynamic data system based on theintegrated data.

With regard to warranty analysis 150, field cases or original datareceived during a warranty period for a vehicle may be analyzed topredict claims or identify root-causes. Further, through warrantyanalysis 150, early warning regarding future problems and/or theforecasting of potential problems may occur. Unstructured data may beconverted to structured data or data in standard format, thus allowingfeatures to be extracted from indexed/ranked cases for analysis.Exemplary features may include vehicle mileage, duration (e.g., months)in service, region, temperature, driver type, fault condition observed,action take by a technician, or the like. Further, multi-dimensionalanalysis is performed on the features to achieve multiple objectives ofthe warranty system. Based on information previously compiled internallywithin the company from customer feedback, such as through field dataprocessing 130, predictions can be made regarding potential parts neededor major problem experienced in the field at a vehicle's estimatedmileage or age, such as at 6000 miles or 3 years.

Original data compiled for warranty analysis 150 may originate withcustomer feedback received by a single or multiple dealers within adealer network, which is then sent to an automotive distributor (e.g.,Toyota). The original data then returns to the warranty or servicedivision of the company. (e.g., Toyota Motor Corporation) where analysesare performed to better predict and forecast problems. Generally,warranty data may be related to any original data associated withidentifying a root cause 152 of a vehicular problem or prediction of awarranty claim, i.e., forecast early warning 154.

Specifically, a warranty claim report may consist of various types ofinformation including temporal, numerical, categorical, and text data.Exemplary temporal data may include production dates, sales dates,repair dates, or the like while numerical data may include mileage,labor cost, part cost, or the like. Examples of categorical data mayinclude, but are not limited to, model number or engine type. Further,text data may comprise consumer complaints, technician reasoning, actiontaken, or the like. Leveraging the knowledge from the unstructured data(e.g., text data) in conjunction with structured data may assist inwarranty analysis to discover defects early in product life-cycle andidentify root-causes of vehicular problems.

Continuing with FIG. 1, within logistics 160, product parts aremaintained and arranged for shipping depending on the servicerequirements. Logistics 160 comprises various subsystems includingproduct parts order 164 and product parts management 162. Product partsorder 164 may manage the ordering of automotive parts while productparts management 162 is involved with the inventory of automotive parts.Logistics 160 may collectively deal with technology whereby automotiveparts are ordered/preordered and managed to meet the requirements at aparticular time. For example, if a new field issue requiring a partreplacement is identified through field data processing 130, or throughforecasting 154 from warranty analysis 150, product parts order 164 mayimplement the order of additional parts to avoid customer inconvenience.

The aforementioned building blocks of service are thus interconnected toprovide integrated analysis from numerous data sources. Further, thebuilding blocks of service may each have one or more associatedobjectives. For example, through data processing 130, productdocumentation 120 may be created. As another example, through field dataprocessing 130, original data related to any vehicular problems may bereceived from a dealer network and analyzed. Further, each of thebuilding blocks or subsystems mentioned previously may employ auditagents, in the form of software agents or human experts, to verify theindividual processes and connections with other systems or subsystems.Generally, from the adaptive service system 100, feedback to design 110reports and quality and customer-satisfaction analysis 140 may begenerated. Specifically, after identifying root-cause indicators andcountermeasures, feedback to design 110 reports may be generated andverified for implementation in a future product.

Generally, the adaptive service system 100 may serve numerous goalsincluding, but not limited to, providing indicators of vehicle and/orsubsystem quality improvement, providing early detection or predictionof vehicle and/or subsystem problems including root-causeidentification, providing increased customer satisfaction and costreduction. Information regarding faulty parts or inadequate service maybe compiled, analyzed, and addressed to result in a reduction of costfor customers. Also, root-cause identification, or finding what iscausing a particular problem, may result in a finding of a designproblem, manufacturing problem, driving condition, road condition, orthe like. In the event of a design or manufacturing problems, measuresmay be taken to result in cost reduction for both manufacturers andcustomers. The adaptive service system 100 collectively integratesheterogeneous original data from various sources and analyzes theoriginal data for multiple applications related to service, such aswarranty, diagnostics, for example, on a large-scale basis.

Moving to FIG. 2, a data warehouse, indicated generally at 200, is shownschematically to implement the adaptive service system 100 shown inFIG. 1. Generally, a data warehouse may be one implementation of adata-mining technique utilized such as in trending and case dataanalysis. The original data 260 may be created or retrieved by varioussources such as direct customer feedback regarding automobiles,automotive parts, or automotive services (e.g., repair services)provided to a dealer or through the use of web crawlers 250 or fielddata collected through dealer networks or through remote services. Webcrawlers 250 may comprise a software application which browses awide-area network (WAN), such as the Internet, to extract informationrelated to products (e.g., automotive) or diagnostic services (e.g.,repairs). Various sources such as consumer blogs, automobile websites,forums, discussion boards, search engines, or the like, may provide partof the original data 260 to be retrieved by web crawlers 250. Forexample, web crawlers 250 may extract data 260 such as discussion amongconsumers related to the release of new vehicles, discussion related tovehicle problems/repairs encountered by self-technicians, or geographicinformation (e.g., location, weather, region) pertaining to vehicleproblems.

In the example of product documentation 120, as shown in FIG. 1,original data 260 is received by a data warehouse 200 from an internaldepartment within a company using a method such as domain semantics, forexample. Original data 260 may comprise various types of informationincluding, but not limited to, engineering data or data relating to themanufacture of a vehicle (e.g., drawings, technical data, computer-aideddesign (CAD) data, data from original equipment manufacturers (OEMs)),surveys (e.g., internal surveys, JD Power), technical assistance data,call center or customer relations data, warranty data, government data(National Highway Traffic Safety Administration (NHTSA)), repair orders(i.e., dealer data), measurement data (sensor data, freeze frame data(FFD), diagnostic data), miscellaneous data (e.g., Microsoft (MS)Office, PDF, hand-written), web data (e.g., discussion forums, blogs,search results), technical documentation (e.g., repair manual service(RMS), field technical reports (FTR), service bulletins (SB), recallnotifications (i.e., service campaigns), metadata (e.g., extensiblemarkup language (XML), or the like.

Generally, the original data 260 may comprise any suitable internaldocumentation, to aftermarket and service data. For example, thepreviously mentioned freeze frame data (FFD) may refer to any parameter,such as vehicle speed, pressure sensor data, temperature, etc., that issaved by the vehicle computer (i.e., control unit) when a vehicleexperiences a problem and a diagnostic trouble code (DTC) is generated.Such information may be collected through remote services or at theautomobile dealerships, which may be used to isolate faults in vehicles.As another example, the aforementioned service bulletin (SB) may referto any addendum to an existing, static repair manual or documentation.Any suitable structured, semi-structured, or unstructured data may beconstituents of the original data 260.

Further, the original data 260 may be correlated with another factor,such as time, to generate a dynamic information database. Particularoriginal data, including engineering data, product documentation, ormiscellaneous data, may be generated and/or gathered at a relativelyearly point in time, such as during the manufacture of a vehicle. Otheroriginal data may be generated and/or gathered at a later point in time,such as during the aftermarket or service duration. Such latter data mayinclude, but are not limited to, technical assistance data, call centeror customer relations data, survey data, or warranty data or web data.

The data warehouse 200 may comprise a data-modeling 210 layer wherebyoriginal data 260 is utilized to dynamically create and/or write repairdocumentation for multiple vehicle types/models identifying the commontypes of repair procedure utilized for each vehicle type/models. Oncethe modeling is completed, data analysis 220 occurs utilizing a varietyof techniques such as data mining, knowledge discovery, or similartechniques implemented in software. Knowledge discovery may generallyrefer to the process of identifying novel, potentially useful, andunderstandable patterns in original data. Further, an application 230may receive the product of the data analysis 220 to make a particularprediction. In the case of the application 230 being warranty analysis150, as seen in FIG. 1, the data analysis 220 may comprise a predictionalgorithm to process the original data 260 from multiple sources topredict a warranty claim. By way of illustration only, incoming originaldata 260 from multiple dealers indicate parts are being replaced forvarious automobile components such as brakes, suspension systems,engines, or transmissions, each component associated with a part number.The original data 260 may be subjected to data analysis 220 to determinefactors related to the claim(s) for replacement parts, whereby thefactors include individual driver behavior, isolated weather conditionsor road surface, or faulty automotive component parts. The datawarehouse 200 may also use system knowledge 240, consisting of domainsemantics 242 and business knowledge 244, along with original data 260,for bringing further domain driven adaptation in data analysis forapplications.

Referring now to FIG. 4, a schematic is provided illustrating dataanalysis 220 to implement the adaptive service system 100 of FIG. 1, inone of its possible implementations. The original data 260 is collectedand cleaned 410, such as by filtering, extraction, or connection ofpertinent information. Any suitable type of information extraction ordata-mining tool may be used to visualize original data to investigatevehicular problems and develop counter-measures to such problems.

Next, the data analysis 220 may involve weighing and ranking of sources420. To this end, sources of the original data 260 are indexed andranked. Field cases or the original data 260 may be ranked usingstatistical techniques and prioritized according to the criticality andalert methods indicated. Data sources may also be weighed and rankeddepending on the application requirements. In an example of identifyingcritical problems, detailed field cases may receive a high rank. In yetanother example, as for locating customer feedback, web data may begiven a high rank. Generally, sources which provide more original data260 for analysis may be given high priority for future analysis.Indexing may be based on factors such as system knowledge 240, location,weather conditions, emerging markets, criticality, or the like. Duringthe process of ranking/indexing original data 260 or data sources,should new knowledge be acquired (e.g., new trends in customer feedbackwith respect to vehicle features, new fault phenomenon, previouslyun-noticed trends in original data), new objectives may be formulated.By way of example, if a new vehicle model is released using a componentfrom a supplier which is also used by other vehicle manufacturers, a newobjective of mining web data may be formulated to determine complaintsfrom consumers.

The original data 260 may then go through additional pre-processing 430involving various steps such as conversion, extraction, indexing, or thelike. For example, the original data 260 may be converted to a standardformat 432 from the original format (e.g., database, MS Office, MSExcel, MS Outlook) in which the original data 260 was received. Theoriginal data 260 collected may include unstructured data (i.e., naturallanguage text) with errors such as misspellings or shortened format, forexample. A language engine may be incorporated at an original equipmentmanufacturer (OEM) information processing system to read the naturallanguage text and convert the original data 260 into structured data instandard format (e.g., with corrected spelling, restructuring ofsentences, etc.) to be integrated with other remaining structured data.Any conventional standard format or format selected by a user 470 may beselected such as MS Excel, XML, semantic, or like.

Pre-processing 430 may also include the extraction of information 434whereby users 470 can determine which format they prefer the originaldata 260 to be converted to and how the original data 260 is extracted.Pre-processing 430 may further include indexing/ranking 436 the originaldata 260 whereby the original data 260 itself is prioritized.Applications 230 or users 470 may also provide feedback to rankingsystems such that if in some cases where certain features play a majorrole, the adaptive service system 100 can send a message to the rankingsystem to place priority on the particular features. Depending onapplication 230 or user 470 requirements, analytical tools 480 may thenbe utilized, such as data mining tools, to process the original data260, i.e., determine root-cause indicators, predict claims, etc.

Similarly to FIG. 2, the data 260 may be processed by variousapplications 230 such as automated search, issue tracking, claimpredictions, troubleshooting help, parts assessment or the like. Theapplications 230 may then implement issue tracking 452 or determineclaim predictions 454 using the analytical tools 480 employed. Asdiscussed previously, system knowledge 240, including business knowledge244, may be utilized by applications in issue tracking 452 and claimpredictions 454.

Continuing with FIG. 4, results 460 of the data analysis 220 may beexhibited in various formats including through visuals 462 and/orreports 464. Suitable forms of, visuals may be presentation slides,graphs, or the like, while conventional reports 464 such as MS Excel, MSPowerPoint, or other report formats may be contemplated.

Referring back to FIG. 2, an application 230 may further utilize systemknowledge 240 to identify the root cause of the claim(s) for replacementparts, for example, to predict upcoming need for the replacement parts.Aspects of system knowledge 240 utilized may include domain semantics242 and business knowledge 244. Collectively, the system knowledge 240manages the three layers, i.e., data-modeling 210, data analysis 220,and applications 230. Collectively, semantic relationships (i.e.,semantic web, semantic net) existing among system knowledge, the threelayers, and original data may form integrated data, thus providing acommon framework whereby original data is shared and reused acrossnumerous applications, enterprises, and community boundaries.

Domain semantics 242 may generally refer to domain knowledge about howsystems, sub-systems, or further units of the systems, areinterconnected. For example, using a vehicle as a system, the powertrain and chassis network may comprise subsystems of the vehicle system.As to be discussed below, a domain taxonomy structure may present avehicle, along with its interconnected subsystems, by name. Thus, in thepresent example, domain semantics 242 relates to knowledge of the powertrain and chassis network, and particularly, how they relate to oneanother or the vehicle system as a whole.

Business knowledge 244, as another aspect of system knowledge 240, mayinclude news or information related to corporate events and/ordecisions. Examples of business knowledge 244 may include, but are notlimited to, a company's decision to cease manufacture of a product(e.g., vehicle) line, the establishment of a new manufacturing orassembly plant location, or the increase/reduction of sales of aparticular product (e.g., vehicle) or subsystem at a particularlocation. Other examples of business knowledge may includepositive/negative feedback from customers in response to features orsubsystems of a particular product (e.g., vehicle) and informationregarding the impact of weather conditions on particular vehicles orsubsystems. By way of illustration only, in the event whereby businessknowledge 244 may include information about the cease of production ofvehicle “x”, all original data regarding vehicle “x” may be removed fromthe system. Alternatively, in the same example, the original datarelated to subsystems (e.g., engines, transmissions) utilized in vehicle“x” maybe analyzed to determine whether they are used in other vehiclemodels and whether the vehicle models experience failure of thesubsystems used in vehicle “x”. Business knowledge may compriseinformation originally retrieved in natural language text to beconverted to other formats, as discussed herein.

Turning now to FIG. 3A, a schematic is provided of an illustrativedomain taxonomy structure, specifically, a vehicle-type domain taxonomy,indicated generally at 300 a. The domain taxonomy structures shownherein may provide illustration of how domain semantics, previouslydiscussed, relates various subsystems or sub-categories to one another.As shown for purposes of illustration only, the generalized category ofa vehicle 310 may be divided into sub-categories of luxury 320 orregular 330 vehicles. Within the luxury 320 sub-category are varioustypes including, but not limited to, utility vehicle 322, sedan 324, orperformance vehicle 326. Alternatively, within the regular 330sub-category of vehicles 310 are cars 332, trucks 334, sport-utilityvehicles (SUVs)/vans 336, or hybrids 338. Cars 332 may be furtherdivided into sedan 340, coupe 342, or convertible 344. It should beunderstood that applicable domain taxonomy structures may include othersub-categories not shown.

Referring now to FIG. 3B, a schematic is provided of anotherillustrative domain taxonomy structure, specifically, a vehiclecomponent domain taxonomy, indicated generally at 300 b. As shown forpurposes of illustration only, the generalized category of a vehicle 310may be divided into subsystems including power train 350, body 360,chassis 370 and network 380. Within the power train 350 subsystem arevarious other sub-subsystems including, but not limited to, engine 352and transmission 354. Likewise, the body 360 may be divided into furthersub-subsystems such as supplemental restraint system (SRS) 362 andair-conditioning 364, for example, as the chassis 370 may be dividedinto tire pressure warning (TPW) system 372 and vehicle stabilitycontrol system (VSC) 374. The network 380 may further include a visual382 and/or audio 384 component. The sub-subsystem engine 352 may bedivided into the fuel system 356 and air intake system (AIS) 357categories. The sub-subsystem transmission 354 may be divided into theclutch 358 and transaxle 359 categories. It should be understood thatapplicable domain taxonomy structures may include other sub-categoriesnot shown.

Generally, domain taxonomy structures may be developed in the datawarehouse 200 and stored in the system knowledge 240. The systemknowledge 240 may then use semantic relationships to develop ahierarchical domain taxonomy utilizing graphical models. Using thisapproach, common concepts and/or relationships found in the systemknowledge 240 may be shared by all product (e.g., vehicle) types, thusreducing redundancy in similar documentation for common products.Product documentation, as discussed previously, may be automaticallycompiled based on the application of a given vehicle using the systemknowledge 240, thus reducing redundancy.

Turning now to FIG. 5, one possible implementation is provided of aportal 500 through which service repair data and/or repair documentationdata is collected and retrieved to/from an information processingsystem. Service repair data collected by utilizing the portal 500 shownmay assist technicians or service specialists in trouble-shootingproblems with vehicles.

The portal 500 may include a search window 510 for a user (e.g.,technician) to input an entry (e.g., keyword, code) that will besearched within the service repair data and/or repair documentation. Theportal 500 may further include an input data window 520 to enable a userto input various information including, but not limited to, vehicledetails (e.g., make, model, year) description of the vehicle problem, orsensor data, to provide detail to the search. For example, to diagnosebrake failure found in a particular vehicle type, a technician may entera keyword such as “brake” and input data 520 including the make andmodel of the vehicle to receive suggestions 530 on how to repair thebrake failure.

In the event that the information processing system may not be able toprovide an immediate repair suggestion upon entry of information intothe search window 510 and/or input data window 520, the system mayprovide dynamic guidance 540 to trouble-shoot the problem. In onepossible implementation, the dynamic guidance 540 may take the form of aseries of questions provided by the information process system inresponse to the user's search and data entries as well as answers inresponse to dynamic guidance questions. A trouble-shooting tree 550 maythen be generated by the system to assist a technician diagnose andrepair a vehicle problem.

The present disclosure contemplates an information processing system,methods, and media for developing semantic relationships among datasources, original data, and knowledge base, thus enabling the sharingand utilization of data across numerous applications and business units.Development of semantic relationships further enables the dynamicupdating of documentation, such as those pertaining to repair services,upon the receipt of incoming dynamic data. Systems, methods, and mediadisclosed herein thus provide improvements to the static nature ofdocumentation related to goods and services, such as in the case ofrepair services documentation, whereby such documentation may bedynamically updated based the updating of data and data sources.

Computer implemented methods of the present disclosure may be carriedout by any instrumentality or aggregate of instrumentalities operable tocompute, classify, process, transmit, receive, retrieve, originate,switch, store, display, manifest, detect, record, reproduce, handle, orutilize any form of information, intelligence, or data for business,scientific, control, or other purposes. For example, a computer orinformation processing system discussed herein may be a personalcomputer, a server, a network storage device, or any other suitabledevice and may vary in size, shape, performance, functionality, andprice. The computer or information processing system may include randomaccess memory (RAM), one or more processing resources such as a centralprocessing unit (CPU) or hardware or software control logic, ROM, and/orother types of nonvolatile memory. Additional components of the computeror information processing system may include one or more disk drives,one or more network ports for communicating with external devices aswell as various input and output (I/O) devices, such as a keyboard, amouse, and a video display. The computer or information processingsystem may also include one or more buses operable to transmit datacommunications between the various hardware components.

Furthermore, methods of the present disclosure, detailed description andclaims may be presented in terms of logic, software or softwareimplemented aspects typically encoded on a variety of storage media ormedium including, but not limited to, computer-readable storagemedium/media, machine-readable storage medium/media, program storagemedium/media or computer program product. Such storage media, havingcomputer-executable instructions, may be handled, read, sensed and/orinterpreted by a computer or information processing system. Generally,computer-executable instructions, such as program modules, may includeroutines, programs, objects, components, data structures, and the like,which perform particular tasks, carry out particular methods orimplement particular abstract data types. Those skilled in the art willappreciate that such media may take various forms such as cards, tapes,magnetic disks (e.g., floppy disk or hard drive) and optical disks(e.g., compact disk read only memory (“CD-ROM”) or digital versatiledisc (“DVD”)). It should be understood that the given implementationsare illustrative only and shall not limit the present disclosure.

Although the present disclosure has been described with reference toparticular examples, embodiments and/or implementations, those skilledin the art will recognize that modifications and variations may be madewithout departing from the spirit and scope of the claimed subjectmatter. Such changes in form and detail, including use of equivalentfunctional and/or structural substitutes for elements described herein,fall within the scope of the appended claims and are intended to becovered by this disclosure.

What is claimed is:
 1. An adaptive information processing system forupdating product documentation and an associated knowledge base specificto multiple automotive vehicular product lines, the system comprising:an adaptive service system interactively and iteratively receivingoriginal data from a data source, the data source including thefollowing: a product documentation source, a field data processingsource, a warranty analysis source and a logistics source anddisseminating integrated data to the product documentation source, fielddata source, warranty analysis source and logistics source, theintegrated data including the original data therein; a processorassociated with the adaptive service system configured to integrate theoriginal data from the product documentation source, the field dataprocessing source, the warranty analysis source and the logistics sourcewithout manual input, and to produce integrated data and update theassociated knowledge base, wherein the integrated data is communicatedto the product documentation source, the field data processing source,the logistics source and the warranty analysis source, wherein theoriginal data is correlated to at least one time factor in a datawarehouse resident in the processor; and wherein the integrated datacommunicated to the product documentation source is included in adaptivedocumentation and reporting, and the integrated data communicated to thefield data processing source is included in automated search tools andin dynamic guidance for trouble-shooting, integrated data communicatedto the logistic source is included in parts order information and partsmanagement systems, and the integrated data communicated to the warrantyanalysis source is included in root-cause identification and earlywarning forecasts.
 2. The adaptive information processing system ofclaim 1 wherein the data warehouse comprises a data modeling layer and adata analysis layer interactively relating to on another, whereinoriginal data is employed in the data modeling layer to dynamicallyproduce automated search tools, dynamic guidance for trouble shooting,parts order information, parts management systems, root causeidentification and early warning forecasts and wherein data analysisoccurring in the data analysis layer includes at least one of datasource weighing and source ranking and data cleaning and collection. 3.The adaptive information processing system of claim 2 wherein dataweighing and ranking includes user input and applications are used toeffect weighing and source ranking, applications including at least oneof the following: issue tracking and claim prediction and wherein datathat has been weighed and source ranked can be subjected to at least onepreprocess including at least one of the following: formatting,information extraction, indexing, ranking.
 4. The adaptive informationprocessing system of claim 3 wherein the preprocessed data can bedispatched to the knowledge base and the knowledge base is queried uponreceipt of updated original data in the data warehouse.
 5. The adaptiveinformation processing of claim 3 wherein system knowledge is inputtedinto the data warehouse and wherein integrated data produced in the datawarehouse by the interactively proceeds through application to updatesystem knowledge.
 6. The system of claim 1, wherein the productdocumentation and the associated knowledge base are related toautomotive repair services, wherein the domain semantics include domaintaxonomy structure regarding interconnected vehicular subsystems.
 7. Thesystem of claim 1, wherein the original data is associated with productparts management or product parts order.
 8. A computer implementedmethod for dynamically updating product documentation and knowledge baseassociated with multiple automotive vehicular product lines, the methodcomprising the steps of: employing an adaptive information processingsystem for updating product documentation and an associated knowledgebase specific to multiple automotive vehicular product lines, the systemincluding: a) an adaptive service system interactively and iterativelyreceiving original data from a data source, the data source includingthe following: a product documentation source, a field data processingsource, a warranty analysis source and a logistics source anddisseminating integrated data to the product documentation source, fielddata source, warranty analysis source and logistics source, theintegrated data including the original data therein; and b) a processorassociated with the adaptive service system configured to integrate theoriginal data from the product documentation source, the field dataprocessing source, the warranty analysis source and the logistics sourcewithout manual input, and to produce integrated data and update theassociated knowledge base, wherein the integrated data is communicatedto the product documentation source, the field data processing source,the logistics source and the warranty analysis source, wherein theoriginal data is correlated to at least one time factor in a datawarehouse resident in the processor; c) wherein the integrated datacommunicated to the product documentation source is included in adaptivedocumentation and reporting, and the integrated data communicated to thefield data processing source is included in automated search tools andin dynamic guidance for trouble-shooting, integrated data communicatedto the logistic source is included in parts order information and partsmanagement systems, and the integrated data communicated to the warrantyanalysis source is included in root-cause identification and earlywarning forecasts; receiving original data associated with multipleautomotive vehicular product lines from the data the data sourceincluding: at least one product documentation source, at least one fielddata processing source, at least one warranty analysis source and atleast one logistics source in a digital data warehouse; integrating,without manual input, the knowledge base with the original data receivedfrom the data sources to form integrated data, the integrating stepoccurring in the digital data warehouse, wherein the integrating stepincludes a data modeling step and a data analysis function, wherein theintegration step is accomplished by the processor; mapping theintegrated data with at least one application, the application externalto the digital data warehouse in communication with system knowledge,wherein the system knowledge is governed by domain semantics andbusiness knowledge; and dynamically updating the knowledge base anddynamically updating the product documentation and the knowledge basebased on integrated data.
 9. The computer implemented method of claim 8wherein the dynamically updated product documentation and knowledge baseintegration step comprises weighing and ranking data inputs by source,the weighing and ranking step including user-initiated inputs andapplications in communication with the digital data warehouse.
 10. Thecomputer implemented method of claim 9 wherein the data that has beenweighed and ranked according to source is subjected to a preprocess inwhich the data is standardized and ranked indexed the preprocessgoverned by user-initiated inputs, analytical tools resident in the datawarehouse and applications external to the digital data warehouse priorto dynamically updating the knowledge base and product documentation.11. The computer implemented method of claim 8 wherein the digital datawarehouse resides in an adaptive service system configured todisseminate integrated data to the following data sources: the at leastone product documentation source, the at least one field data processingsource, the at least one warranty analysis source and the at least onelogistics source in a digital data warehouse, wherein the productdocumentation including integrated data produces adaptive documentationand reporting, and wherein the field data processing source producesdynamic guidance for trouble-shooting and automated search, and whereinthe logistics source disseminated parts orders and parts management andwherein the warranty analysis source produces early warning forecastsand root cause identification.
 12. The computer implemented method ofclaim 8 wherein the original data is shared and reused acrossapplications and community boundaries.
 13. The computer implementedmethod of claim 8 wherein at least a portion of the original data isderived from data mined from at least one of the following: blogs,discussion boards, on-line forums, field data, regulatory agencies,customer feedback regarding automobiles, customer feedback regardingautomotive parts, customer feedback regarding automotive services,wherein the original data comprises data related to identifying a rootcause of a vehicular problem or prediction of a warranty claim.
 14. Thecomputer implemented method of claim 9 wherein, collectively, systemknowledge manages data modeling, domain semantics, and application andsemantic relationships existing among system knowledge, data modeling,domain semantics and application and original data to form theintegrated data.
 15. The method of claim 12 wherein the data sourcecomprises field data in association with data mined from at least one ofthe following; data associated with regulatory agencies, customerfeedback regarding automobiles, customer feedback regarding automotiveparts, customer feedback regarding automotive services.
 16. The methodof claim 11, wherein the product documentation is associated withautomotive repair services.
 17. The method of claim 16, wherein theoriginal data is associated with parts management or parts order. 18.The method of claim 8, wherein integrating the knowledge base with theoriginal data comprises: establishing a semantic relationship among theknowledge base and the original data; and dynamically updating theproduct documentation based on the semantic relationship.
 19. The methodof claim 18, further comprising: utilizing web crawlers to extract dataassociated with products or diagnostic services; and dynamicallyupdating the product documentation and the knowledge base based on thedata extracted.
 20. The method of claim 8, wherein the original datacomprises warranty data related to identifying a root cause of avehicular problem or prediction of a warranty claim.
 21. The method ofclaim 8, wherein the original data is associated with parts managementor parts order.